Using regression plots
NEST CoreDev
Source:vignettes/using-regression-plots.Rmd
using-regression-plots.Rmd
teal
application to use regression plot with various
datasets types
This vignette will guide you through the four parts to create a
teal
application using various types of datasets using the
regression plot module tm_a_regression()
:
- Load libraries
- Create data sets
- Create an
app
variable - Run the app
1 - Load libraries
library(teal.modules.general) # used to create the app
library(dplyr) # used to modify data sets
2 - Create data sets
Inside this app 4 datasets will be used
-
ADSL
A wide data set with subject data -
ADRS
A long data set with response data for subjects at different time points of the study -
ADTTE
A long data set with time to event data -
ADLB
A long data set with lab measurements for each subject
data <- teal_data()
data <- within(data, {
ADSL <- teal.data::rADSL %>%
mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1))
ADRS <- teal.data::rADRS
ADTTE <- teal.data::rADTTE
ADLB <- teal.data::rADLB %>%
mutate(CHGC = as.factor(case_when(
CHG < 1 ~ "N",
CHG > 1 ~ "P",
TRUE ~ "-"
)))
})
join_keys(data) <- default_cdisc_join_keys[names(data)]
3 - Create an app
variable
This is the most important section. We will use the
teal::init()
function to create an app. The data will be
handed over using teal.data::teal_data()
. The app itself
will be constructed by multiple calls of tm_a_regression()
using different combinations of data sets.
# configuration for the single wide dataset
mod1 <- tm_a_regression(
label = "Single wide dataset",
response = data_extract_spec(
dataname = "ADSL",
select = select_spec(
label = "Select variable:",
choices = variable_choices(data[["ADSL"]], c("BMRKR1", "BMRKR2")),
selected = "BMRKR1",
multiple = FALSE,
fixed = FALSE
)
),
regressor = data_extract_spec(
dataname = "ADSL",
select = select_spec(
label = "Select variables:",
choices = variable_choices(data[["ADSL"]], c("AGE", "SEX", "RACE")),
selected = "AGE",
multiple = TRUE,
fixed = FALSE
)
)
)
# configuration for the two wide datasets
mod2 <- tm_a_regression(
label = "Two wide datasets",
default_plot_type = 2,
response = data_extract_spec(
dataname = "ADSL",
select = select_spec(
label = "Select variable:",
choices = variable_choices(data[["ADSL"]], c("BMRKR1", "BMRKR2")),
selected = "BMRKR1",
multiple = FALSE,
fixed = FALSE
)
),
regressor = data_extract_spec(
dataname = "ADSL",
select = select_spec(
label = "Select variables:",
choices = variable_choices(data[["ADSL"]], c("AGE", "SEX", "RACE")),
selected = c("AGE", "RACE"),
multiple = TRUE,
fixed = FALSE
)
)
)
# configuration for the same long datasets (same subset)
mod3 <- tm_a_regression(
label = "Same long datasets (same subset)",
default_plot_type = 2,
response = data_extract_spec(
dataname = "ADTTE",
select = select_spec(
label = "Select variable:",
choices = variable_choices(data[["ADTTE"]], c("AVAL", "CNSR")),
selected = "AVAL",
multiple = FALSE,
fixed = FALSE
),
filter = filter_spec(
label = "Select parameter:",
vars = "PARAMCD",
choices = value_choices(data[["ADTTE"]], "PARAMCD", "PARAM"),
selected = "PFS",
multiple = FALSE
)
),
regressor = data_extract_spec(
dataname = "ADTTE",
select = select_spec(
label = "Select variable:",
choices = variable_choices(data[["ADTTE"]], c("AGE", "CNSR", "SEX")),
selected = c("AGE", "CNSR", "SEX"),
multiple = TRUE
),
filter = filter_spec(
label = "Select parameter:",
vars = "PARAMCD",
choices = value_choices(data[["ADTTE"]], "PARAMCD", "PARAM"),
selected = "PFS",
multiple = FALSE
)
)
)
# configuration for the wide and long datasets
mod4 <- tm_a_regression(
label = "Wide and long datasets",
response = data_extract_spec(
dataname = "ADLB",
filter = list(
filter_spec(
vars = "PARAMCD",
choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"),
selected = levels(data[["ADLB"]]$PARAMCD)[2],
multiple = TRUE,
label = "Select measurement:"
),
filter_spec(
vars = "AVISIT",
choices = levels(data[["ADLB"]]$AVISIT),
selected = levels(data[["ADLB"]]$AVISIT)[2],
multiple = TRUE,
label = "Select visit:"
)
),
select = select_spec(
label = "Select variable:",
choices = "AVAL",
selected = "AVAL",
multiple = FALSE,
fixed = TRUE
)
),
regressor = data_extract_spec(
dataname = "ADSL",
select = select_spec(
label = "Select variables:",
choices = variable_choices(data[["ADSL"]], c("BMRKR1", "BMRKR2", "AGE")),
selected = "AGE",
multiple = TRUE,
fixed = FALSE
)
)
)
# configuration for the same long datasets (different subsets)
mod5 <- tm_a_regression(
label = "Same long datasets (different subsets)",
default_plot_type = 2,
response = data_extract_spec(
dataname = "ADLB",
filter = list(
filter_spec(
vars = "PARAMCD",
choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"),
selected = levels(data[["ADLB"]]$PARAMCD)[1],
multiple = TRUE,
label = "Select lab:"
),
filter_spec(
vars = "AVISIT",
choices = levels(data[["ADLB"]]$AVISIT),
selected = levels(data[["ADLB"]]$AVISIT)[1],
multiple = TRUE,
label = "Select visit:"
)
),
select = select_spec(
choices = "AVAL",
selected = "AVAL",
multiple = FALSE,
fixed = TRUE
)
),
regressor = data_extract_spec(
dataname = "ADLB",
filter = list(
filter_spec(
vars = "PARAMCD",
choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"),
selected = levels(data[["ADLB"]]$PARAMCD)[1],
multiple = FALSE,
label = "Select labs:"
),
filter_spec(
vars = "AVISIT",
choices = levels(data[["ADLB"]]$AVISIT),
selected = levels(data[["ADLB"]]$AVISIT)[1],
multiple = FALSE,
label = "Select visit:"
)
),
select = select_spec(
choices = variable_choices(data[["ADLB"]], c("AVAL", "AGE", "BMRKR1", "BMRKR2", "SEX", "ARM")),
selected = c("AVAL", "BMRKR1"),
multiple = TRUE
)
)
)
# initialize the app
app <- init(
data = data,
modules = modules(
modules(
label = "Regression plots",
mod1,
mod2,
mod3,
mod4,
mod5
)
)
)
4 - Run the app
A simple shiny::shinyApp()
call will let you run the
app. Note that app is only displayed when running this code inside an
R
session.
shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))